Wavelet-Based Fractal Analysis of the Epileptic EEG Signal

The wavelet transform is a natural tool for characterizing self-similar signals. In this work, the spectral exponent gamma derived from the wavelet-based representation for 1/f processes is used to investigate the self-similarity of electrocorticography (intracranial EEG) signals from an epilepsy patient. An increase in gamma leads to sample signals with smoother temporal patterns. Our computational results show that during an epileptic seizure gamma is significantly higher than that associated with other states of the brain, implying that wavelet-based fractal analysis is potentially a useful computational tool for epileptic seizure detection.